Datasets:

projecte-aina
/

viquiquad

	"""ViquiQuAD Dataset."""
	# Loading script for the ViquiQuAD dataset.
	import json

	import datasets

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """\
	Rodriguez-Penagos, Carlos Gerardo, & Armentano-Oller, Carme. (2021).
	ViquiQuAD: an extractive QA dataset from Catalan Wikipedia (Version ViquiQuad_v.1.0.1)
	[Data set]. Zenodo. http://doi.org/10.5281/zenodo.4761412
	"""

	_DESCRIPTION = """\
	ViquiQuAD: an extractive QA dataset from Catalan Wikipedia.
	This dataset contains 3111 contexts extracted from a set of 597 high quality original (no translations)
	articles in the Catalan Wikipedia "Viquipèdia" (ca.wikipedia.org), and 1 to 5 questions with their
	answer for each fragment. Viquipedia articles are used under CC-by-sa licence.
	This dataset can be used to build extractive-QA and Language Models.
	Funded by the Generalitat de Catalunya, Departament de Polítiques Digitals i Administració Pública (AINA),
	MT4ALL and Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
	"""

	_HOMEPAGE = "https://zenodo.org/record/4562345#.YK41aqGxWUk"

	_URL = "https://huggingface.co/datasets/projecte-aina/viquiquad/resolve/main/"
	_TRAINING_FILE = "train.json"
	_DEV_FILE = "dev.json"
	_TEST_FILE = "test.json"


	class ViquiQuAD(datasets.GeneratorBasedBuilder):
	"""ViquiQuAD Dataset."""

	VERSION = datasets.Version("1.0.1")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answers": [
	{
	"text": datasets.Value("string"),
	"answer_start": datasets.Value("int32"),
	}
	],
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	"train": f"{_URL}{_TRAINING_FILE}",
	"dev": f"{_URL}{_DEV_FILE}",
	"test": f"{_URL}{_TEST_FILE}",
	}
	downloaded_files = dl_manager.download(urls_to_download)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	logger.info("generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	viquiquad = json.load(f)
	for article in viquiquad["data"]:
	title = article.get("title", "").strip()
	for paragraph in article["paragraphs"]:
	context = paragraph["context"].strip()
	for qa in paragraph["qas"]:
	question = qa["question"].strip()
	id_ = qa["id"]
	# answer_starts = [answer["answer_start"] for answer in qa["answers"]]
	# answers = [answer["text"].strip() for answer in qa["answers"]]
	text = qa["answers"][0]["text"]
	answer_start = qa["answers"][0]["answer_start"]

	# Features currently used are "context", "question", and "answers".
	# Others are extracted here for the ease of future expansions.
	yield id_, {
	"title": title,
	"context": context,
	"question": question,
	"id": id_,
	"answers": [{"text": text, "answer_start": answer_start}],
	}